neural coding & computation group
Principal Investigator | |
Jonathan Pillow
Jonathan is a professor in the Princeton Neuroscience Institute (PNI) and Department of Psychology, with an affiliation to the Center for Statistics & Machine Learning. He received a Ph.D. in neural science from NYU (supervised by Eero Simoncelli), and was a postdoc at the Gatsby Computational Neuroscience Unit at UCL. Jonathan was an assistant professor at UT Austin (2009-2014) before moving to Princeton in 2014. | |
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Postdocs | |
Ben Cowley
Ben has a PhD in machine learning from Carnegie Mellon University, and joined the lab as a C.V. Starr Fellow in September 2018. Ben has a background in developing and applying dimensionality reduction techniques to neural data. Currently, he is focused on developing new adaptive stimulus selection algorithms to better train models that predict neural responses and behavior from stimulus features. | |
Matthew Creamer (joint with Andy Leifer) Matt is a C. V. Starr Fellow at the Princeton Neuroscience Institute and is jointly advised by Andrew Leifer and Jonathan Pillow. He is currently working on modeling whole-brain calcium dynamics and characterizing functional changes between neurons during learning in C. elegans. Matt received his PhD from Yale, working with Damon Clark studying how animals detect visual motion cues and use these cues to regulate their walking speed. When not in lab, he enjoys rock climbing, running, dungeons and dragons, and video games | |
Brian
DePasquale (joint with Carlos Brody) Brian completed a Ph.D. in Neurobiology and Behavior at Columbia University with Larry Abbott. His work focused on the dynamics of recurrent spiking and continuous variable neural networks and the development of methods for training these networks to perform tasks and to replicate experimental data. His current work focuses on latent variable models of behavior and neural activity during evidence accumulation. General areas of interests in theoretical neuroscience include the role of random and learned connections in neural circuit function and sources of variability in network dynamics and its relationship to behavior. | |
Zeinab Mohammadi
Zeinab received her Masters and PhD in Electrical Engineering from the University of Colorado, where she developed a new real-time spike sorting algorithm (EGNG) to analyze the High-Density Microelectrode Array (HD-MEA) data such as Neuropixels probe data. Generally, she is interested in the intersection of machine learning, signal processing and computational neuroscience to develop algorithms for analyzing the neural activity. Zeinab's current research includes using GLM-HMM to model animal behaviors and multi-region neural analysis methods. | |
Rich Pang (joint with Mala Murthy, Josh Shaevitz, & Bill
Bialek) Rich received his Ph.D. in neuroscience from the University of Washington, where he used computational techniques to address a variety of questions about animal behavior and neural information processing. He is currently working on theory-driven analysis and modeling methods for understanding acoustic communication and its neural substrates. More generally, he is interested in applying techniques from physics, statistical modeling, and network science to understand how neural circuits represent and manipulate the many complex information structures making up day-to-day experience. | |
Abby Russo (joint with Carlos Brody) Abby completed a Ph.D. in Neuroscience at Columbia University under Mark Churchland. There, she developed analytic tools for identifying computation-dependent signatures of neural dynamics in primary motor cortex and in the supplementary motor area. Broadly, Abby is interested in understanding general principles of circuit-level neural computation through large-scale neural recordings and computational modeling. | |
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Students | |
Zoe
Ashwood Zoe is a 4th-year Ph.D. student in the Computer Science department. Prior to coming to Princeton, Zoe obtained her undergraduate degree in math and physics at the University of St Andrews in Scotland, and worked for two years as a Research Fellow at Stanford. Zoe is interested in using Bayesian inference to find structure in neural spike train data and to improve experimental design. | |
Kevin Chen (joint with Andy Leifer) Kevin is a 4th-year PhD student in PNI, with B.S. degree from National Taiwan University and M.S. research at Academia Sinica, where he studied predictive coding in the retina. After coming to Princeton, he was fascinated by neural dynamics and behavior in C. elegans. He is broadly interested in statistical models for animal behavior, biophysical models, and dynamics in neural networks. | |
Daniel Greenidge Daniel is a 1st-year PhD student in Computer Science. Daniel graduated with a B.S.E. from Princetonâ€™s Operations Research and Financial Engineering department in 2018, and joined the Pillow lab as a research assistant in 2019. Currently, he is working on scaling Gaussian process models of latent neural structure to large datasets. | |
Orren Karniol-Tambour Orren is a 3rd-year PhD Student in PNI, with an MS in Symbolic Systems from Stanford University and BA in Economics from Brandeis University. Previously, he worked on encoding models for a retinal prosthesis with EJ Chichilnisky at Stanford, and spent time doing research in industry, at an infectious disease genomics startup and a hedge fund. His current research focus includes statistical modeling of multi-region neural activity underlying cognitive behavior. | |
Aditi
Jha Aditi is a 2nd-year PhD student in Electrical Engineering with a B.Tech in Electrical Engineering from Indian Institute of Technology Delhi, where she worked on compositionality in convolutional neural networks with Sumeet Agarwal. She is interested in generative models, Bayesian statistics and their applicability in understanding neural data. Her current research focuses on sufficient dimensional reduction for fMRI using a generative approach. | |
Mike Morais Mike is a 5th-year PhD student in PNI, with B.S. degrees in Bioengineering and Mathematics from the University of Pittsburgh, where he studied attention and population coding in the primate visual system with Matt Smith. He also studied decision making in the mouse visual system at the RIKEN Brain Science Institute with Andrea Benucci. His research interests include Bayesian models of perception, dimension reduction for fMRI data, and associative learning models of psychological phenomena. | |
Yoel Sanchez
Araujo (joint with Nathaniel Daw) I'm a second year PhD student at PNI. Broadly speaking, I'm interested in the intersection between Neuroscience and Artificial Intelligence, and statistics. I'm particularly interested in leveraging ideas and methods from Reinforcement Learning and Bayesian models of behavior and cognition to inform Neuroscientific investigations. Currently I am jointly advised by Professors Jonathan Pillow and Nathaniel Daw, and closely collaborate with Professor Ilana Witten. Before starting my PhD I was a research assistant working with Professor Nathaniel Daw at Princeton. My work for the most part involved writing out a Bayesian model of change point detection and before that I worked at MIT for 2 years as an RA. | |
Iris
Stone (joint with Ilana Witten) Iris is a 3rd-year Ph.D. student in PNI with a B.S. in Physics from George Mason University, where she studied the use of organic and nanomaterials for applications in biomedicine and neuroscience. Broadly speaking, her interests include understanding both the neural circuitry and behavior that support decision-making and social interactions. Her current work includes using latent-state models to identify the discrete structures underlying these cognitive processes. | |
David Zoltowski David is a 4th-year Ph.D. student in PNI, with B.S. in electrical engineering from Michigan State University and an M.Phil. degree in Engineering from the University of Cambridge, where he worked with Máté Lengyel on perceptual decision-making. His research interests include statistical models of neural population and behavioral data and perceptual decision-making. | |
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